[3062] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
[16057] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
[3062] | 4 | *
|
---|
| 5 | * This file is part of HeuristicLab.
|
---|
| 6 | *
|
---|
| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
| 8 | * it under the terms of the GNU General Public License as published by
|
---|
| 9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
| 10 | * (at your option) any later version.
|
---|
| 11 | *
|
---|
| 12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
| 15 | * GNU General Public License for more details.
|
---|
| 16 | *
|
---|
| 17 | * You should have received a copy of the GNU General Public License
|
---|
| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
| 19 | */
|
---|
| 20 | #endregion
|
---|
| 21 |
|
---|
| 22 | using System;
|
---|
| 23 | using System.Collections.Generic;
|
---|
[4722] | 24 | using HeuristicLab.Common;
|
---|
[3062] | 25 | using HeuristicLab.Core;
|
---|
| 26 | using HeuristicLab.Data;
|
---|
| 27 | using HeuristicLab.Parameters;
|
---|
[4068] | 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
[3062] | 29 |
|
---|
| 30 | namespace HeuristicLab.Encodings.BinaryVectorEncoding {
|
---|
| 31 | /// <summary>
|
---|
| 32 | /// N point crossover for binary vectors.
|
---|
| 33 | /// </summary>
|
---|
| 34 | /// <remarks>
|
---|
| 35 | /// It is implemented as described in Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg..
|
---|
| 36 | /// </remarks>
|
---|
| 37 | [Item("NPointCrossover", "N point crossover for binary vectors. It is implemented as described in Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg.")]
|
---|
| 38 | [StorableClass]
|
---|
[4722] | 39 | public sealed class NPointCrossover : BinaryVectorCrossover {
|
---|
[3062] | 40 | /// <summary>
|
---|
| 41 | /// Number of crossover points.
|
---|
| 42 | /// </summary>
|
---|
[11909] | 43 | public IValueLookupParameter<IntValue> NParameter {
|
---|
| 44 | get { return (IValueLookupParameter<IntValue>)Parameters["N"]; }
|
---|
[3062] | 45 | }
|
---|
| 46 |
|
---|
[4722] | 47 | [StorableConstructor]
|
---|
| 48 | private NPointCrossover(bool deserializing) : base(deserializing) { }
|
---|
| 49 | private NPointCrossover(NPointCrossover original, Cloner cloner) : base(original, cloner) { }
|
---|
[3062] | 50 | /// <summary>
|
---|
| 51 | /// Initializes a new instance of <see cref="NPointCrossover"/>
|
---|
| 52 | /// </summary>
|
---|
[4722] | 53 | public NPointCrossover()
|
---|
| 54 | : base() {
|
---|
[3065] | 55 | Parameters.Add(new ValueLookupParameter<IntValue>("N", "Number of crossover points", new IntValue(2)));
|
---|
[3062] | 56 | }
|
---|
| 57 |
|
---|
[4722] | 58 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 59 | return new NPointCrossover(this, cloner);
|
---|
| 60 | }
|
---|
| 61 |
|
---|
[3062] | 62 | /// <summary>
|
---|
| 63 | /// Performs a N point crossover at randomly chosen positions of the two
|
---|
| 64 | /// given parent binary vectors.
|
---|
| 65 | /// </summary>
|
---|
| 66 | /// <exception cref="ArgumentException">Thrown when the value for N is invalid or when the parent vectors are of different length.</exception>
|
---|
| 67 | /// <param name="random">A random number generator.</param>
|
---|
| 68 | /// <param name="parent1">The first parent for crossover.</param>
|
---|
| 69 | /// <param name="parent2">The second parent for crossover.</param>
|
---|
| 70 | /// <param name="n">Number of crossover points.</param>
|
---|
| 71 | /// <returns>The newly created binary vector, resulting from the N point crossover.</returns>
|
---|
| 72 | public static BinaryVector Apply(IRandom random, BinaryVector parent1, BinaryVector parent2, IntValue n) {
|
---|
| 73 | if (parent1.Length != parent2.Length)
|
---|
| 74 | throw new ArgumentException("NPointCrossover: The parents are of different length.");
|
---|
| 75 |
|
---|
| 76 | if (n.Value > parent1.Length)
|
---|
| 77 | throw new ArgumentException("NPointCrossover: There cannot be more breakpoints than the size of the parents.");
|
---|
| 78 |
|
---|
| 79 | if (n.Value < 1)
|
---|
| 80 | throw new ArgumentException("NPointCrossover: N cannot be < 1.");
|
---|
| 81 |
|
---|
| 82 | int length = parent1.Length;
|
---|
| 83 | bool[] result = new bool[length];
|
---|
| 84 | int[] breakpoints = new int[n.Value];
|
---|
| 85 |
|
---|
| 86 | //choose break points
|
---|
| 87 | List<int> breakpointPool = new List<int>();
|
---|
[4068] | 88 |
|
---|
[3062] | 89 | for (int i = 0; i < length; i++)
|
---|
| 90 | breakpointPool.Add(i);
|
---|
| 91 |
|
---|
| 92 | for (int i = 0; i < n.Value; i++) {
|
---|
| 93 | int index = random.Next(breakpointPool.Count);
|
---|
| 94 | breakpoints[i] = breakpointPool[index];
|
---|
| 95 | breakpointPool.RemoveAt(index);
|
---|
| 96 | }
|
---|
| 97 |
|
---|
| 98 | Array.Sort(breakpoints);
|
---|
| 99 |
|
---|
| 100 | //perform crossover
|
---|
| 101 | int arrayIndex = 0;
|
---|
| 102 | int breakPointIndex = 0;
|
---|
| 103 | bool firstParent = true;
|
---|
| 104 |
|
---|
| 105 | while (arrayIndex < length) {
|
---|
[4068] | 106 | if (breakPointIndex < breakpoints.Length &&
|
---|
[3062] | 107 | arrayIndex == breakpoints[breakPointIndex]) {
|
---|
| 108 | breakPointIndex++;
|
---|
| 109 | firstParent = !firstParent;
|
---|
| 110 | }
|
---|
| 111 |
|
---|
| 112 | if (firstParent)
|
---|
| 113 | result[arrayIndex] = parent1[arrayIndex];
|
---|
| 114 | else
|
---|
| 115 | result[arrayIndex] = parent2[arrayIndex];
|
---|
| 116 |
|
---|
| 117 | arrayIndex++;
|
---|
| 118 | }
|
---|
| 119 |
|
---|
| 120 | return new BinaryVector(result);
|
---|
| 121 | }
|
---|
| 122 |
|
---|
| 123 | /// <summary>
|
---|
| 124 | /// Performs a N point crossover at a randomly chosen position of two
|
---|
| 125 | /// given parent binary vectors.
|
---|
| 126 | /// </summary>
|
---|
| 127 | /// <exception cref="ArgumentException">Thrown if there are not exactly two parents.</exception>
|
---|
| 128 | /// <exception cref="InvalidOperationException">
|
---|
| 129 | /// Thrown when the N parameter could not be found.</description></item>
|
---|
| 130 | /// </exception>
|
---|
| 131 | /// <param name="random">A random number generator.</param>
|
---|
| 132 | /// <param name="parents">An array containing the two binary vectors that should be crossed.</param>
|
---|
| 133 | /// <returns>The newly created binary vector, resulting from the N point crossover.</returns>
|
---|
| 134 | protected override BinaryVector Cross(IRandom random, ItemArray<BinaryVector> parents) {
|
---|
| 135 | if (parents.Length != 2) throw new ArgumentException("ERROR in NPointCrossover: The number of parents is not equal to 2");
|
---|
| 136 |
|
---|
| 137 | if (NParameter.ActualValue == null) throw new InvalidOperationException("NPointCrossover: Parameter " + NParameter.ActualName + " could not be found.");
|
---|
| 138 |
|
---|
[11909] | 139 | return Apply(random, parents[0], parents[1], NParameter.ActualValue);
|
---|
[3062] | 140 | }
|
---|
| 141 | }
|
---|
| 142 | }
|
---|